Overview

Dataset statistics

Number of variables16
Number of observations19001
Missing cells7531
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 MiB
Average record size in memory459.0 B

Variable types

Numeric10
Categorical5
DateTime1

Alerts

name has a high cardinality: 18780 distinct valuesHigh cardinality
host_name has a high cardinality: 6307 distinct valuesHigh cardinality
neighbourhood has a high cardinality: 215 distinct valuesHigh cardinality
id is highly overall correlated with host_idHigh correlation
host_id is highly overall correlated with idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
last_review has 3758 (19.8%) missing valuesMissing
reviews_per_month has 3758 (19.8%) missing valuesMissing
minimum_nights is highly skewed (γ1 = 26.36588078)Skewed
name is uniformly distributedUniform
id has unique valuesUnique
number_of_reviews has 3758 (19.8%) zerosZeros
availability_365 has 6970 (36.7%) zerosZeros

Reproduction

Analysis started2024-06-13 08:30:44.671537
Analysis finished2024-06-13 08:30:51.180869
Duration6.51 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19001
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18830405
Minimum2539
Maximum36485609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:51.219028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1177971
Q19355498
median19387536
Q328919516
95-th percentile35240782
Maximum36485609
Range36483070
Interquartile range (IQR)19564018

Descriptive statistics

Standard deviation10969858
Coefficient of variation (CV)0.5825609
Kurtosis-1.2256633
Mean18830405
Median Absolute Deviation (MAD)9804926
Skewness-0.066873715
Sum3.5779653 × 1011
Variance1.2033778 × 1014
MonotonicityNot monotonic
2024-06-13T09:30:51.278805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9138664 1
 
< 0.1%
17922181 1
 
< 0.1%
13746962 1
 
< 0.1%
20917712 1
 
< 0.1%
26734288 1
 
< 0.1%
10527546 1
 
< 0.1%
9518 1
 
< 0.1%
12188651 1
 
< 0.1%
28959608 1
 
< 0.1%
15158194 1
 
< 0.1%
Other values (18991) 18991
99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5121 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
5803 1
< 0.1%
6090 1
< 0.1%
6848 1
< 0.1%
7750 1
< 0.1%
ValueCountFrequency (%)
36485609 1
< 0.1%
36485057 1
< 0.1%
36480292 1
< 0.1%
36479723 1
< 0.1%
36478343 1
< 0.1%
36472171 1
< 0.1%
36471896 1
< 0.1%
36468880 1
< 0.1%
36458668 1
< 0.1%
36456829 1
< 0.1%

name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct18780
Distinct (%)98.9%
Missing7
Missing (%)< 0.1%
Memory size1.9 MiB
Hillside Hotel
 
7
Brooklyn Apartment
 
7
Home away from home
 
6
Private Room
 
6
New york Multi-unit building
 
5
Other values (18775)
18963 

Length

Max length179
Median length66
Mean length36.751395
Min length1

Characters and Unicode

Total characters698056
Distinct characters504
Distinct categories20 ?
Distinct scripts11 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18619 ?
Unique (%)98.0%

Sample

1st rowPrivate Lg Room 15 min to Manhattan
2nd rowTIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC
3rd rowVoted #1 Location Quintessential 1BR W Village Apt
4th rowSpacious 1 bedroom apartment 15min from Manhattan
5th rowBig beautiful bedroom in huge Bushwick apartment

Common Values

ValueCountFrequency (%)
Hillside Hotel 7
 
< 0.1%
Brooklyn Apartment 7
 
< 0.1%
Home away from home 6
 
< 0.1%
Private Room 6
 
< 0.1%
New york Multi-unit building 5
 
< 0.1%
Cozy Room 5
 
< 0.1%
Private room in Manhattan 5
 
< 0.1%
Private room in Williamsburg 4
 
< 0.1%
Cozy Private Room 4
 
< 0.1%
Williamsburg Loft 3
 
< 0.1%
Other values (18770) 18942
99.7%
(Missing) 7
 
< 0.1%

Length

2024-06-13T09:30:51.349702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in 6594
 
5.7%
room 4014
 
3.5%
3161
 
2.7%
bedroom 3016
 
2.6%
private 2874
 
2.5%
apartment 2658
 
2.3%
cozy 2017
 
1.7%
apt 1765
 
1.5%
brooklyn 1623
 
1.4%
studio 1556
 
1.3%
Other values (6673) 86285
74.7%

Most occurring characters

ValueCountFrequency (%)
97219
 
13.9%
e 48065
 
6.9%
o 47895
 
6.9%
t 40878
 
5.9%
a 40431
 
5.8%
r 38293
 
5.5%
i 36980
 
5.3%
n 36723
 
5.3%
l 20035
 
2.9%
m 19434
 
2.8%
Other values (494) 272103
39.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 469006
67.2%
Uppercase Letter 102950
 
14.7%
Space Separator 97221
 
13.9%
Other Punctuation 12924
 
1.9%
Decimal Number 9521
 
1.4%
Dash Punctuation 2620
 
0.4%
Other Letter 976
 
0.1%
Math Symbol 970
 
0.1%
Close Punctuation 621
 
0.1%
Open Punctuation 568
 
0.1%
Other values (10) 679
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36
 
3.7%
22
 
2.3%
21
 
2.2%
19
 
1.9%
18
 
1.8%
18
 
1.8%
16
 
1.6%
15
 
1.5%
15
 
1.5%
13
 
1.3%
Other values (299) 783
80.2%
Lowercase Letter
ValueCountFrequency (%)
e 48065
 
10.2%
o 47895
 
10.2%
t 40878
 
8.7%
a 40431
 
8.6%
r 38293
 
8.2%
i 36980
 
7.9%
n 36723
 
7.8%
l 20035
 
4.3%
m 19434
 
4.1%
s 18729
 
4.0%
Other values (48) 121543
25.9%
Other Symbol
ValueCountFrequency (%)
95
28.5%
66
19.8%
31
 
9.3%
19
 
5.7%
14
 
4.2%
13
 
3.9%
13
 
3.9%
8
 
2.4%
7
 
2.1%
6
 
1.8%
Other values (27) 61
18.3%
Uppercase Letter
ValueCountFrequency (%)
B 11484
 
11.2%
S 10080
 
9.8%
C 8176
 
7.9%
A 7545
 
7.3%
R 6917
 
6.7%
P 5740
 
5.6%
E 5345
 
5.2%
L 5288
 
5.1%
M 4520
 
4.4%
N 4373
 
4.2%
Other values (23) 33482
32.5%
Other Punctuation
ValueCountFrequency (%)
, 3504
27.1%
! 3055
23.6%
/ 1958
15.2%
. 1683
13.0%
& 1234
 
9.5%
' 425
 
3.3%
* 329
 
2.5%
: 225
 
1.7%
# 212
 
1.6%
" 111
 
0.9%
Other values (9) 188
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 3413
35.8%
2 2526
26.5%
3 931
 
9.8%
5 800
 
8.4%
0 768
 
8.1%
4 459
 
4.8%
6 233
 
2.4%
8 150
 
1.6%
7 147
 
1.5%
9 94
 
1.0%
Math Symbol
ValueCountFrequency (%)
+ 476
49.1%
| 359
37.0%
~ 99
 
10.2%
= 11
 
1.1%
> 9
 
0.9%
< 7
 
0.7%
6
 
0.6%
2
 
0.2%
× 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 596
96.0%
] 17
 
2.7%
} 4
 
0.6%
3
 
0.5%
1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 544
95.8%
[ 16
 
2.8%
{ 4
 
0.7%
3
 
0.5%
1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 2589
98.8%
16
 
0.6%
15
 
0.6%
Space Separator
ValueCountFrequency (%)
97219
> 99.9%
  2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
89
86.4%
14
 
13.6%
Nonspacing Mark
ValueCountFrequency (%)
66
88.0%
9
 
12.0%
Initial Punctuation
ValueCountFrequency (%)
15
78.9%
4
 
21.1%
Modifier Letter
ValueCountFrequency (%)
7
70.0%
3
30.0%
Modifier Symbol
ValueCountFrequency (%)
^ 6
85.7%
` 1
 
14.3%
Control
ValueCountFrequency (%)
72
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 34
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%
Other Number
ValueCountFrequency (%)
² 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 571873
81.9%
Common 125049
 
17.9%
Han 878
 
0.1%
Inherited 75
 
< 0.1%
Cyrillic 71
 
< 0.1%
Katakana 45
 
< 0.1%
Hebrew 31
 
< 0.1%
Georgian 12
 
< 0.1%
Hiragana 12
 
< 0.1%
Hangul 8
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
36
 
4.1%
22
 
2.5%
21
 
2.4%
19
 
2.2%
18
 
2.1%
18
 
2.1%
16
 
1.8%
15
 
1.7%
15
 
1.7%
13
 
1.5%
Other values (244) 685
78.0%
Common
ValueCountFrequency (%)
97219
77.7%
, 3504
 
2.8%
1 3413
 
2.7%
! 3055
 
2.4%
- 2589
 
2.1%
2 2526
 
2.0%
/ 1958
 
1.6%
. 1683
 
1.3%
& 1234
 
1.0%
3 931
 
0.7%
Other values (92) 6937
 
5.5%
Latin
ValueCountFrequency (%)
e 48065
 
8.4%
o 47895
 
8.4%
t 40878
 
7.1%
a 40431
 
7.1%
r 38293
 
6.7%
i 36980
 
6.5%
n 36723
 
6.4%
l 20035
 
3.5%
m 19434
 
3.4%
s 18729
 
3.3%
Other values (61) 224410
39.2%
Katakana
ValueCountFrequency (%)
7
15.6%
5
 
11.1%
3
 
6.7%
3
 
6.7%
3
 
6.7%
3
 
6.7%
2
 
4.4%
2
 
4.4%
1
 
2.2%
1
 
2.2%
Other values (15) 15
33.3%
Cyrillic
ValueCountFrequency (%)
а 10
14.1%
т 7
9.9%
н 7
9.9%
с 6
 
8.5%
о 6
 
8.5%
к 5
 
7.0%
е 4
 
5.6%
м 4
 
5.6%
р 3
 
4.2%
я 3
 
4.2%
Other values (9) 16
22.5%
Hebrew
ValueCountFrequency (%)
ו 5
16.1%
י 5
16.1%
ב 4
12.9%
ר 4
12.9%
ה 2
 
6.5%
ת 2
 
6.5%
ע 2
 
6.5%
א 1
 
3.2%
מ 1
 
3.2%
ס 1
 
3.2%
Other values (4) 4
12.9%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Hiragana
ValueCountFrequency (%)
5
41.7%
2
 
16.7%
1
 
8.3%
1
 
8.3%
1
 
8.3%
1
 
8.3%
1
 
8.3%
Inherited
ValueCountFrequency (%)
66
88.0%
9
 
12.0%
Georgian
ValueCountFrequency (%)
12
100.0%
Devanagari
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 696288
99.7%
CJK 878
 
0.1%
Punctuation 187
 
< 0.1%
None 179
 
< 0.1%
Misc Symbols 176
 
< 0.1%
Dingbats 130
 
< 0.1%
VS 75
 
< 0.1%
Cyrillic 71
 
< 0.1%
Hebrew 31
 
< 0.1%
Georgian 12
 
< 0.1%
Other values (7) 29
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
97219
 
14.0%
e 48065
 
6.9%
o 47895
 
6.9%
t 40878
 
5.9%
a 40431
 
5.8%
r 38293
 
5.5%
i 36980
 
5.3%
n 36723
 
5.3%
l 20035
 
2.9%
m 19434
 
2.8%
Other values (86) 270335
38.8%
Misc Symbols
ValueCountFrequency (%)
95
54.0%
31
 
17.6%
14
 
8.0%
6
 
3.4%
5
 
2.8%
4
 
2.3%
3
 
1.7%
3
 
1.7%
2
 
1.1%
2
 
1.1%
Other values (6) 11
 
6.2%
Punctuation
ValueCountFrequency (%)
89
47.6%
34
 
18.2%
16
 
8.6%
15
 
8.0%
15
 
8.0%
14
 
7.5%
4
 
2.1%
VS
ValueCountFrequency (%)
66
88.0%
9
 
12.0%
Dingbats
ValueCountFrequency (%)
66
50.8%
13
 
10.0%
13
 
10.0%
8
 
6.2%
7
 
5.4%
5
 
3.8%
4
 
3.1%
4
 
3.1%
3
 
2.3%
2
 
1.5%
Other values (3) 5
 
3.8%
CJK
ValueCountFrequency (%)
36
 
4.1%
22
 
2.5%
21
 
2.4%
19
 
2.2%
18
 
2.1%
18
 
2.1%
16
 
1.8%
15
 
1.7%
15
 
1.7%
13
 
1.5%
Other values (244) 685
78.0%
None
ValueCountFrequency (%)
19
 
10.6%
à 15
 
8.4%
ó 9
 
5.0%
· 7
 
3.9%
é 7
 
3.9%
7
 
3.9%
7
 
3.9%
7
 
3.9%
² 6
 
3.4%
6
 
3.4%
Other values (51) 89
49.7%
Georgian
ValueCountFrequency (%)
12
100.0%
Cyrillic
ValueCountFrequency (%)
а 10
14.1%
т 7
9.9%
н 7
9.9%
с 6
 
8.5%
о 6
 
8.5%
к 5
 
7.0%
е 4
 
5.6%
м 4
 
5.6%
р 3
 
4.2%
я 3
 
4.2%
Other values (9) 16
22.5%
Hebrew
ValueCountFrequency (%)
ו 5
16.1%
י 5
16.1%
ב 4
12.9%
ר 4
12.9%
ה 2
 
6.5%
ת 2
 
6.5%
ע 2
 
6.5%
א 1
 
3.2%
מ 1
 
3.2%
ס 1
 
3.2%
Other values (4) 4
12.9%
Hiragana
ValueCountFrequency (%)
5
41.7%
2
 
16.7%
1
 
8.3%
1
 
8.3%
1
 
8.3%
1
 
8.3%
1
 
8.3%
Geometric Shapes
ValueCountFrequency (%)
2
100.0%
Math Operators
ValueCountFrequency (%)
2
100.0%
Devanagari
ValueCountFrequency (%)
2
100.0%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Misc Technical
ValueCountFrequency (%)
1
50.0%
1
50.0%

host_id
Real number (ℝ)

Distinct16241
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66394589
Minimum2571
Maximum2.7427328 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:51.418930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2571
5-th percentile764688
Q17728754
median30487854
Q31.0483536 × 108
95-th percentile2.3951918 × 108
Maximum2.7427328 × 108
Range2.7427071 × 108
Interquartile range (IQR)97106602

Descriptive statistics

Standard deviation77826632
Coefficient of variation (CV)1.1721834
Kurtosis0.28986658
Mean66394589
Median Absolute Deviation (MAD)27185317
Skewness1.2459588
Sum1.2615636 × 1012
Variance6.0569847 × 1015
MonotonicityNot monotonic
2024-06-13T09:30:51.471889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 117
 
0.6%
107434423 71
 
0.4%
30283594 44
 
0.2%
137358866 36
 
0.2%
12243051 36
 
0.2%
61391963 34
 
0.2%
16098958 33
 
0.2%
22541573 32
 
0.2%
26377263 24
 
0.1%
2856748 22
 
0.1%
Other values (16231) 18552
97.6%
ValueCountFrequency (%)
2571 1
 
< 0.1%
2787 3
< 0.1%
3151 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
3647 2
< 0.1%
4396 1
 
< 0.1%
4869 1
 
< 0.1%
5089 1
 
< 0.1%
6041 1
 
< 0.1%
ValueCountFrequency (%)
274273284 1
< 0.1%
274195458 1
< 0.1%
274103383 1
< 0.1%
273870123 1
< 0.1%
273841667 1
< 0.1%
273741577 1
< 0.1%
273656890 1
< 0.1%
273632292 1
< 0.1%
273619304 1
< 0.1%
273613106 1
< 0.1%

host_name
Categorical

Distinct6307
Distinct (%)33.2%
Missing8
Missing (%)< 0.1%
Memory size1.3 MiB
Michael
 
159
David
 
157
John
 
130
Sonder (NYC)
 
117
Alex
 
98
Other values (6302)
18332 

Length

Max length35
Median length31
Mean length6.1009319
Min length1

Characters and Unicode

Total characters115875
Distinct characters138
Distinct categories11 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4077 ?
Unique (%)21.5%

Sample

1st rowIris
2nd rowJohlex
3rd rowJohn
4th rowRegan
5th rowMegan

Common Values

ValueCountFrequency (%)
Michael 159
 
0.8%
David 157
 
0.8%
John 130
 
0.7%
Sonder (NYC) 117
 
0.6%
Alex 98
 
0.5%
Daniel 92
 
0.5%
Sarah 87
 
0.5%
Maria 86
 
0.5%
Chris 81
 
0.4%
Anna 77
 
0.4%
Other values (6297) 17909
94.3%

Length

2024-06-13T09:30:51.529444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
401
 
1.9%
and 234
 
1.1%
michael 175
 
0.8%
david 169
 
0.8%
sonder 153
 
0.7%
john 145
 
0.7%
nyc 122
 
0.6%
alex 121
 
0.6%
laura 117
 
0.6%
maria 105
 
0.5%
Other values (5877) 19369
91.7%

Most occurring characters

ValueCountFrequency (%)
a 14760
 
12.7%
e 11187
 
9.7%
i 9409
 
8.1%
n 9394
 
8.1%
r 6989
 
6.0%
l 5914
 
5.1%
o 4954
 
4.3%
t 3638
 
3.1%
s 3533
 
3.0%
h 3518
 
3.0%
Other values (128) 42579
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91499
79.0%
Uppercase Letter 21211
 
18.3%
Space Separator 2158
 
1.9%
Other Punctuation 579
 
0.5%
Open Punctuation 134
 
0.1%
Close Punctuation 134
 
0.1%
Dash Punctuation 76
 
0.1%
Other Letter 39
 
< 0.1%
Decimal Number 30
 
< 0.1%
Math Symbol 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14760
16.1%
e 11187
12.2%
i 9409
10.3%
n 9394
10.3%
r 6989
 
7.6%
l 5914
 
6.5%
o 4954
 
5.4%
t 3638
 
4.0%
s 3533
 
3.9%
h 3518
 
3.8%
Other values (46) 18203
19.9%
Uppercase Letter
ValueCountFrequency (%)
A 2475
11.7%
J 2094
 
9.9%
M 2051
 
9.7%
S 1821
 
8.6%
C 1432
 
6.8%
L 1124
 
5.3%
D 1079
 
5.1%
K 1024
 
4.8%
R 989
 
4.7%
E 937
 
4.4%
Other values (22) 6185
29.2%
Other Letter
ValueCountFrequency (%)
3
 
7.7%
3
 
7.7%
3
 
7.7%
3
 
7.7%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
Other values (17) 17
43.6%
Other Punctuation
ValueCountFrequency (%)
& 415
71.7%
. 122
 
21.1%
/ 14
 
2.4%
, 13
 
2.2%
' 7
 
1.2%
! 4
 
0.7%
@ 3
 
0.5%
: 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
5 7
23.3%
7 5
16.7%
0 5
16.7%
1 4
13.3%
2 3
10.0%
4 3
10.0%
6 2
 
6.7%
3 1
 
3.3%
Space Separator
ValueCountFrequency (%)
2154
99.8%
4
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%
Math Symbol
ValueCountFrequency (%)
+ 14
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112687
97.2%
Common 3126
 
2.7%
Han 34
 
< 0.1%
Cyrillic 23
 
< 0.1%
Hiragana 3
 
< 0.1%
Hangul 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14760
 
13.1%
e 11187
 
9.9%
i 9409
 
8.3%
n 9394
 
8.3%
r 6989
 
6.2%
l 5914
 
5.2%
o 4954
 
4.4%
t 3638
 
3.2%
s 3533
 
3.1%
h 3518
 
3.1%
Other values (63) 39391
35.0%
Common
ValueCountFrequency (%)
2154
68.9%
& 415
 
13.3%
( 134
 
4.3%
) 134
 
4.3%
. 122
 
3.9%
- 76
 
2.4%
+ 14
 
0.4%
/ 14
 
0.4%
, 13
 
0.4%
5 7
 
0.2%
Other values (13) 43
 
1.4%
Han
ValueCountFrequency (%)
3
 
8.8%
3
 
8.8%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
Other values (12) 12
35.3%
Cyrillic
ValueCountFrequency (%)
А 3
13.0%
е 3
13.0%
л 2
 
8.7%
и 2
 
8.7%
й 2
 
8.7%
н 2
 
8.7%
р 1
 
4.3%
Ю 1
 
4.3%
т 1
 
4.3%
а 1
 
4.3%
Other values (5) 5
21.7%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115704
99.9%
None 105
 
0.1%
CJK 34
 
< 0.1%
Cyrillic 23
 
< 0.1%
Punctuation 4
 
< 0.1%
Hiragana 3
 
< 0.1%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14760
 
12.8%
e 11187
 
9.7%
i 9409
 
8.1%
n 9394
 
8.1%
r 6989
 
6.0%
l 5914
 
5.1%
o 4954
 
4.3%
t 3638
 
3.1%
s 3533
 
3.1%
h 3518
 
3.0%
Other values (63) 42408
36.7%
None
ValueCountFrequency (%)
é 42
40.0%
á 10
 
9.5%
í 9
 
8.6%
ë 8
 
7.6%
è 6
 
5.7%
ô 6
 
5.7%
ú 5
 
4.8%
ç 2
 
1.9%
ï 2
 
1.9%
ı 2
 
1.9%
Other values (12) 13
 
12.4%
Punctuation
ValueCountFrequency (%)
4
100.0%
CJK
ValueCountFrequency (%)
3
 
8.8%
3
 
8.8%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
Other values (12) 12
35.3%
Cyrillic
ValueCountFrequency (%)
А 3
13.0%
е 3
13.0%
л 2
 
8.7%
и 2
 
8.7%
й 2
 
8.7%
н 2
 
8.7%
р 1
 
4.3%
Ю 1
 
4.3%
т 1
 
4.3%
а 1
 
4.3%
Other values (5) 5
21.7%
Hiragana
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Brooklyn
8046 
Manhattan
8031 
Queens
2331 
Bronx
 
434
Staten Island
 
159

Length

Max length13
Median length9
Mean length8.1506237
Min length5

Characters and Unicode

Total characters154870
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowManhattan
3rd rowManhattan
4th rowQueens
5th rowBrooklyn

Common Values

ValueCountFrequency (%)
Brooklyn 8046
42.3%
Manhattan 8031
42.3%
Queens 2331
 
12.3%
Bronx 434
 
2.3%
Staten Island 159
 
0.8%

Length

2024-06-13T09:30:51.581495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-13T09:30:51.633237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 8046
42.0%
manhattan 8031
41.9%
queens 2331
 
12.2%
bronx 434
 
2.3%
staten 159
 
0.8%
island 159
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 27191
17.6%
a 24411
15.8%
o 16526
10.7%
t 16380
10.6%
r 8480
 
5.5%
B 8480
 
5.5%
l 8205
 
5.3%
y 8046
 
5.2%
k 8046
 
5.2%
M 8031
 
5.2%
Other values (10) 21074
13.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135551
87.5%
Uppercase Letter 19160
 
12.4%
Space Separator 159
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 27191
20.1%
a 24411
18.0%
o 16526
12.2%
t 16380
12.1%
r 8480
 
6.3%
l 8205
 
6.1%
y 8046
 
5.9%
k 8046
 
5.9%
h 8031
 
5.9%
e 4821
 
3.6%
Other values (4) 5414
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 8480
44.3%
M 8031
41.9%
Q 2331
 
12.2%
S 159
 
0.8%
I 159
 
0.8%
Space Separator
ValueCountFrequency (%)
159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154711
99.9%
Common 159
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 27191
17.6%
a 24411
15.8%
o 16526
10.7%
t 16380
10.6%
r 8480
 
5.5%
B 8480
 
5.5%
l 8205
 
5.3%
y 8046
 
5.2%
k 8046
 
5.2%
M 8031
 
5.2%
Other values (9) 20915
13.5%
Common
ValueCountFrequency (%)
159
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 154870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 27191
17.6%
a 24411
15.8%
o 16526
10.7%
t 16380
10.6%
r 8480
 
5.5%
B 8480
 
5.5%
l 8205
 
5.3%
y 8046
 
5.2%
k 8046
 
5.2%
M 8031
 
5.2%
Other values (10) 21074
13.6%

neighbourhood
Categorical

Distinct215
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Williamsburg
1526 
Bedford-Stuyvesant
1478 
Harlem
 
1086
Bushwick
 
978
Upper West Side
 
734
Other values (210)
13199 

Length

Max length26
Median length17
Mean length11.919478
Min length4

Characters and Unicode

Total characters226482
Distinct characters54
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.1%

Sample

1st rowSunnyside
2nd rowHell's Kitchen
3rd rowWest Village
4th rowAstoria
5th rowBushwick

Common Values

ValueCountFrequency (%)
Williamsburg 1526
 
8.0%
Bedford-Stuyvesant 1478
 
7.8%
Harlem 1086
 
5.7%
Bushwick 978
 
5.1%
Upper West Side 734
 
3.9%
East Village 705
 
3.7%
Hell's Kitchen 693
 
3.6%
Upper East Side 667
 
3.5%
Crown Heights 631
 
3.3%
Midtown 505
 
2.7%
Other values (205) 9998
52.6%

Length

2024-06-13T09:30:51.679993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
east 2575
 
8.4%
side 1756
 
5.7%
harlem 1540
 
5.0%
williamsburg 1526
 
5.0%
bedford-stuyvesant 1478
 
4.8%
heights 1427
 
4.7%
upper 1401
 
4.6%
village 1186
 
3.9%
west 1033
 
3.4%
bushwick 978
 
3.2%
Other values (227) 15760
51.4%

Most occurring characters

ValueCountFrequency (%)
e 20714
 
9.1%
i 16226
 
7.2%
s 15652
 
6.9%
t 15047
 
6.6%
a 14816
 
6.5%
l 13284
 
5.9%
r 13260
 
5.9%
11659
 
5.1%
n 10202
 
4.5%
o 9383
 
4.1%
Other values (44) 86239
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 179812
79.4%
Uppercase Letter 32558
 
14.4%
Space Separator 11659
 
5.1%
Dash Punctuation 1707
 
0.8%
Other Punctuation 746
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20714
11.5%
i 16226
 
9.0%
s 15652
 
8.7%
t 15047
 
8.4%
a 14816
 
8.2%
l 13284
 
7.4%
r 13260
 
7.4%
n 10202
 
5.7%
o 9383
 
5.2%
d 7624
 
4.2%
Other values (15) 43604
24.2%
Uppercase Letter
ValueCountFrequency (%)
H 4667
14.3%
S 4437
13.6%
B 3298
10.1%
W 3154
9.7%
E 2772
8.5%
C 2107
 
6.5%
G 1464
 
4.5%
U 1429
 
4.4%
F 1324
 
4.1%
V 1208
 
3.7%
Other values (14) 6698
20.6%
Other Punctuation
ValueCountFrequency (%)
' 697
93.4%
. 48
 
6.4%
, 1
 
0.1%
Space Separator
ValueCountFrequency (%)
11659
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 212370
93.8%
Common 14112
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20714
 
9.8%
i 16226
 
7.6%
s 15652
 
7.4%
t 15047
 
7.1%
a 14816
 
7.0%
l 13284
 
6.3%
r 13260
 
6.2%
n 10202
 
4.8%
o 9383
 
4.4%
d 7624
 
3.6%
Other values (39) 76162
35.9%
Common
ValueCountFrequency (%)
11659
82.6%
- 1707
 
12.1%
' 697
 
4.9%
. 48
 
0.3%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20714
 
9.1%
i 16226
 
7.2%
s 15652
 
6.9%
t 15047
 
6.6%
a 14816
 
6.5%
l 13284
 
5.9%
r 13260
 
5.9%
11659
 
5.1%
n 10202
 
4.5%
o 9383
 
4.1%
Other values (44) 86239
38.1%

latitude
Real number (ℝ)

Distinct12087
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728063
Minimum40.50873
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:51.730809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum40.50873
5-th percentile40.64456
Q140.68882
median40.72171
Q340.76321
95-th percentile40.82645
Maximum40.91306
Range0.40433
Interquartile range (IQR)0.07439

Descriptive statistics

Standard deviation0.05538931
Coefficient of variation (CV)0.001359979
Kurtosis0.05864488
Mean40.728063
Median Absolute Deviation (MAD)0.03659
Skewness0.2542518
Sum773873.93
Variance0.0030679757
MonotonicityNot monotonic
2024-06-13T09:30:51.788892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 8
 
< 0.1%
40.72232 8
 
< 0.1%
40.68634 8
 
< 0.1%
40.69414 8
 
< 0.1%
40.68683 7
 
< 0.1%
40.72607 7
 
< 0.1%
40.68084 7
 
< 0.1%
40.72434 6
 
< 0.1%
40.76389 6
 
< 0.1%
40.70741 6
 
< 0.1%
Other values (12077) 18930
99.6%
ValueCountFrequency (%)
40.50873 1
< 0.1%
40.52293 1
< 0.1%
40.53871 1
< 0.1%
40.53939 1
< 0.1%
40.54106 1
< 0.1%
40.54312 1
< 0.1%
40.5455 1
< 0.1%
40.54857 1
< 0.1%
40.54889 1
< 0.1%
40.54901 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.90527 1
< 0.1%
40.90391 1
< 0.1%
40.90356 1
< 0.1%
40.90329 1
< 0.1%
40.90281 1
< 0.1%
40.9026 1
< 0.1%
40.89981 1
< 0.1%
40.89811 1
< 0.1%
40.89756 1
< 0.1%

longitude
Real number (ℝ)

Distinct9944
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.950827
Minimum-74.23914
Maximum-73.71795
Zeros0
Zeros (%)0.0%
Negative19001
Negative (%)100.0%
Memory size296.9 KiB
2024-06-13T09:30:51.846430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-74.23914
5-th percentile-74.00369
Q1-73.98205
median-73.95463
Q3-73.93449
95-th percentile-73.86186
Maximum-73.71795
Range0.52119
Interquartile range (IQR)0.04756

Descriptive statistics

Standard deviation0.046824987
Coefficient of variation (CV)-0.00063319085
Kurtosis4.8242797
Mean-73.950827
Median Absolute Deviation (MAD)0.02489
Skewness1.2340208
Sum-1405139.7
Variance0.0021925794
MonotonicityNot monotonic
2024-06-13T09:30:51.904922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.94829 9
 
< 0.1%
-73.95725 9
 
< 0.1%
-73.95121 9
 
< 0.1%
-73.95742 9
 
< 0.1%
-73.95427 9
 
< 0.1%
-73.98589 9
 
< 0.1%
-73.95675 8
 
< 0.1%
-73.9567 8
 
< 0.1%
-73.95149 8
 
< 0.1%
-73.98043 8
 
< 0.1%
Other values (9934) 18915
99.5%
ValueCountFrequency (%)
-74.23914 1
< 0.1%
-74.21238 1
< 0.1%
-74.19626 1
< 0.1%
-74.18259 1
< 0.1%
-74.17628 1
< 0.1%
-74.17388 1
< 0.1%
-74.17117 1
< 0.1%
-74.17065 1
< 0.1%
-74.16966 1
< 0.1%
-74.16634 1
< 0.1%
ValueCountFrequency (%)
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.72582 1
< 0.1%
-73.72716 1
< 0.1%
-73.72731 1
< 0.1%
-73.7274 1
< 0.1%
-73.72778 1
< 0.1%
-73.72817 1
< 0.1%
-73.72901 1
< 0.1%
-73.72928 1
< 0.1%

room_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Entire home/apt
9522 
Private room
9041 
Shared room
 
438

Length

Max length15
Median length15
Mean length13.480343
Min length11

Characters and Unicode

Total characters256140
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 9522
50.1%
Private room 9041
47.6%
Shared room 438
 
2.3%

Length

2024-06-13T09:30:51.955349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-13T09:30:52.004967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 9522
25.1%
home/apt 9522
25.1%
room 9479
24.9%
private 9041
23.8%
shared 438
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 28523
11.1%
o 28480
11.1%
r 28480
11.1%
t 28085
11.0%
a 19001
 
7.4%
19001
 
7.4%
m 19001
 
7.4%
i 18563
 
7.2%
h 9960
 
3.9%
p 9522
 
3.7%
Other values (7) 47524
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 208616
81.4%
Space Separator 19001
 
7.4%
Uppercase Letter 19001
 
7.4%
Other Punctuation 9522
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28523
13.7%
o 28480
13.7%
r 28480
13.7%
t 28085
13.5%
a 19001
9.1%
m 19001
9.1%
i 18563
8.9%
h 9960
 
4.8%
p 9522
 
4.6%
n 9522
 
4.6%
Other values (2) 9479
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
E 9522
50.1%
P 9041
47.6%
S 438
 
2.3%
Space Separator
ValueCountFrequency (%)
19001
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 9522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 227617
88.9%
Common 28523
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28523
12.5%
o 28480
12.5%
r 28480
12.5%
t 28085
12.3%
a 19001
8.3%
m 19001
8.3%
i 18563
8.2%
h 9960
 
4.4%
p 9522
 
4.2%
E 9522
 
4.2%
Other values (5) 28480
12.5%
Common
ValueCountFrequency (%)
19001
66.6%
/ 9522
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28523
11.1%
o 28480
11.1%
r 28480
11.1%
t 28085
11.0%
a 19001
 
7.4%
19001
 
7.4%
m 19001
 
7.4%
i 18563
 
7.2%
h 9960
 
3.9%
p 9522
 
3.7%
Other values (7) 47524
18.6%

price
Real number (ℝ)

Distinct321
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.34046
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:52.051571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q166
median100
Q3160
95-th percentile270
Maximum350
Range340
Interquartile range (IQR)94

Descriptive statistics

Standard deviation71.530346
Coefficient of variation (CV)0.58468268
Kurtosis0.50280069
Mean122.34046
Median Absolute Deviation (MAD)45
Skewness1.0270244
Sum2324591
Variance5116.5903
MonotonicityNot monotonic
2024-06-13T09:30:52.104543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 856
 
4.5%
150 821
 
4.3%
50 636
 
3.3%
200 590
 
3.1%
75 570
 
3.0%
60 555
 
2.9%
80 531
 
2.8%
70 482
 
2.5%
120 471
 
2.5%
65 471
 
2.5%
Other values (311) 13018
68.5%
ValueCountFrequency (%)
10 6
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
16 3
 
< 0.1%
18 1
 
< 0.1%
19 3
 
< 0.1%
20 13
0.1%
21 3
 
< 0.1%
ValueCountFrequency (%)
350 147
0.8%
349 14
 
0.1%
348 1
 
< 0.1%
347 2
 
< 0.1%
346 1
 
< 0.1%
345 11
 
0.1%
344 1
 
< 0.1%
343 2
 
< 0.1%
342 1
 
< 0.1%
341 2
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct75
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9068996
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:52.159974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)4

Descriptive statistics

Standard deviation21.456544
Coefficient of variation (CV)3.1065377
Kurtosis1156.4552
Mean6.9068996
Median Absolute Deviation (MAD)1
Skewness26.365881
Sum131238
Variance460.38328
MonotonicityNot monotonic
2024-06-13T09:30:52.215567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5003
26.3%
2 4619
24.3%
3 3086
16.2%
30 1442
 
7.6%
4 1253
 
6.6%
5 1140
 
6.0%
7 822
 
4.3%
6 285
 
1.5%
14 210
 
1.1%
10 178
 
0.9%
Other values (65) 963
 
5.1%
ValueCountFrequency (%)
1 5003
26.3%
2 4619
24.3%
3 3086
16.2%
4 1253
 
6.6%
5 1140
 
6.0%
6 285
 
1.5%
7 822
 
4.3%
8 52
 
0.3%
9 34
 
0.2%
10 178
 
0.9%
ValueCountFrequency (%)
1250 1
 
< 0.1%
999 2
 
< 0.1%
480 1
 
< 0.1%
400 1
 
< 0.1%
370 1
 
< 0.1%
365 11
0.1%
364 1
 
< 0.1%
300 1
 
< 0.1%
299 1
 
< 0.1%
240 2
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct321
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.797747
Minimum0
Maximum607
Zeros3758
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:52.388741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q324
95-th percentile116
Maximum607
Range607
Interquartile range (IQR)23

Descriptive statistics

Standard deviation45.493455
Coefficient of variation (CV)1.9116706
Kurtosis19.565921
Mean23.797747
Median Absolute Deviation (MAD)6
Skewness3.7060188
Sum452181
Variance2069.6544
MonotonicityNot monotonic
2024-06-13T09:30:52.439424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3758
19.8%
1 2022
 
10.6%
2 1342
 
7.1%
3 975
 
5.1%
4 796
 
4.2%
5 589
 
3.1%
6 544
 
2.9%
7 495
 
2.6%
8 449
 
2.4%
9 383
 
2.0%
Other values (311) 7648
40.3%
ValueCountFrequency (%)
0 3758
19.8%
1 2022
10.6%
2 1342
 
7.1%
3 975
 
5.1%
4 796
 
4.2%
5 589
 
3.1%
6 544
 
2.9%
7 495
 
2.6%
8 449
 
2.4%
9 383
 
2.0%
ValueCountFrequency (%)
607 1
< 0.1%
594 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
474 1
< 0.1%
467 1
< 0.1%
466 1
< 0.1%
459 1
< 0.1%
448 1
< 0.1%
441 1
< 0.1%
Distinct1494
Distinct (%)9.8%
Missing3758
Missing (%)19.8%
Memory size296.9 KiB
Minimum2011-05-12 00:00:00
Maximum2019-07-08 00:00:00
2024-06-13T09:30:52.494618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:52.551385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct789
Distinct (%)5.2%
Missing3758
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean1.3809276
Minimum0.01
Maximum27.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:52.606247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.01
95-th percentile4.69
Maximum27.95
Range27.94
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.6899884
Coefficient of variation (CV)1.2238066
Kurtosis11.9516
Mean1.3809276
Median Absolute Deviation (MAD)0.62
Skewness2.4353306
Sum21049.48
Variance2.8560608
MonotonicityNot monotonic
2024-06-13T09:30:52.655711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 362
 
1.9%
1 352
 
1.9%
0.05 333
 
1.8%
0.03 323
 
1.7%
0.04 268
 
1.4%
0.08 256
 
1.3%
0.16 244
 
1.3%
0.09 235
 
1.2%
0.06 226
 
1.2%
0.11 217
 
1.1%
Other values (779) 12427
65.4%
(Missing) 3758
 
19.8%
ValueCountFrequency (%)
0.01 17
 
0.1%
0.02 362
1.9%
0.03 323
1.7%
0.04 268
1.4%
0.05 333
1.8%
0.06 226
1.2%
0.07 168
0.9%
0.08 256
1.3%
0.09 235
1.2%
0.1 191
1.0%
ValueCountFrequency (%)
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.22 1
< 0.1%
13.45 1
< 0.1%
13.42 1
< 0.1%
13.24 1
< 0.1%
13.15 1
< 0.1%
12.99 1
< 0.1%
Distinct47
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5838114
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:52.708970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile13
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation31.15475
Coefficient of variation (CV)4.7320235
Kurtosis77.214814
Mean6.5838114
Median Absolute Deviation (MAD)0
Skewness8.4612229
Sum125099
Variance970.61846
MonotonicityNot monotonic
2024-06-13T09:30:52.760742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 12627
66.5%
2 2605
 
13.7%
3 1123
 
5.9%
4 539
 
2.8%
5 345
 
1.8%
6 208
 
1.1%
7 165
 
0.9%
8 164
 
0.9%
327 117
 
0.6%
9 98
 
0.5%
Other values (37) 1010
 
5.3%
ValueCountFrequency (%)
1 12627
66.5%
2 2605
 
13.7%
3 1123
 
5.9%
4 539
 
2.8%
5 345
 
1.8%
6 208
 
1.1%
7 165
 
0.9%
8 164
 
0.9%
9 98
 
0.5%
10 69
 
0.4%
ValueCountFrequency (%)
327 117
0.6%
232 71
0.4%
121 44
 
0.2%
103 36
 
0.2%
96 69
0.4%
91 34
 
0.2%
87 32
 
0.2%
65 19
 
0.1%
52 41
 
0.2%
50 16
 
0.1%

availability_365
Real number (ℝ)

Distinct366
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.72538
Minimum0
Maximum365
Zeros6970
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size296.9 KiB
2024-06-13T09:30:52.819686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39
Q3219
95-th percentile358
Maximum365
Range365
Interquartile range (IQR)219

Descriptive statistics

Standard deviation130.5999
Coefficient of variation (CV)1.1902433
Kurtosis-0.93482914
Mean109.72538
Median Absolute Deviation (MAD)39
Skewness0.80270185
Sum2084892
Variance17056.334
MonotonicityNot monotonic
2024-06-13T09:30:52.873572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6970
36.7%
365 432
 
2.3%
364 195
 
1.0%
1 176
 
0.9%
5 133
 
0.7%
3 115
 
0.6%
2 113
 
0.6%
179 112
 
0.6%
89 111
 
0.6%
6 110
 
0.6%
Other values (356) 10534
55.4%
ValueCountFrequency (%)
0 6970
36.7%
1 176
 
0.9%
2 113
 
0.6%
3 115
 
0.6%
4 107
 
0.6%
5 133
 
0.7%
6 110
 
0.6%
7 96
 
0.5%
8 85
 
0.4%
9 83
 
0.4%
ValueCountFrequency (%)
365 432
2.3%
364 195
1.0%
363 88
 
0.5%
362 62
 
0.3%
361 44
 
0.2%
360 33
 
0.2%
359 49
 
0.3%
358 53
 
0.3%
357 33
 
0.2%
356 26
 
0.1%

Interactions

2024-06-13T09:30:50.212369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.481699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.000400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.487582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.014223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.641584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.164936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.685311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.226099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.708871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.265256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.535549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.050699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.541161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.199638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.696193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.218274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.762187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.276078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.758502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.441917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.586163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.095350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.590864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.245503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.746863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.268303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.817230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.323544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.807963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.493884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.642187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.147254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.645991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.299452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.804525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.324995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.874978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.374934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.862882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.545565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.692722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.193853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.697706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.345799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.858820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.378380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.927704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.420786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.912005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.596342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.744524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.243546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.749651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.396319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.911329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.431898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.977733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.468169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.963458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.648751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.794770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.293284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.801333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.445528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.962696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.483611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.028289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.515857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.013922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.701156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.847230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.340502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.855597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.494378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.012938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.535921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.075957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.563122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.063480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.750792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.896411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.388918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.907643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.540374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.063255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.584595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.123621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.609525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.111516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.816107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:45.947832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.436437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:46.961691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:47.590434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.113674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:48.634282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.173476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:49.657681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-06-13T09:30:50.161473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-06-13T09:30:52.922541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365neighbourhood_grouproom_type
id1.0000.552-0.0080.080-0.046-0.073-0.3020.3650.1360.1570.0600.073
host_id0.5521.0000.0350.123-0.102-0.147-0.1140.2710.1420.1640.1020.099
latitude-0.0080.0351.0000.0480.1320.016-0.032-0.030-0.002-0.0200.5370.104
longitude0.0800.1230.0481.000-0.418-0.1230.0720.1270.0680.0800.6470.142
price-0.046-0.1020.132-0.4181.0000.106-0.020-0.020-0.1180.0590.1810.497
minimum_nights-0.073-0.1470.016-0.1230.1061.000-0.164-0.2920.0630.0780.0000.021
number_of_reviews-0.302-0.114-0.0320.072-0.020-0.1641.0000.7070.0660.2570.0260.000
reviews_per_month0.3650.271-0.0300.127-0.020-0.2920.7071.0000.1530.3970.0670.016
calculated_host_listings_count0.1360.142-0.0020.068-0.1180.0630.0660.1531.0000.4110.0850.094
availability_3650.1570.164-0.0200.0800.0590.0780.2570.3970.4111.0000.0790.093
neighbourhood_group0.0600.1020.5370.6470.1810.0000.0260.0670.0850.0791.0000.115
room_type0.0730.0990.1040.1420.4970.0210.0000.0160.0940.0930.1151.000

Missing values

2024-06-13T09:30:50.907028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-13T09:30:51.033329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-13T09:30:51.135863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
09138664Private Lg Room 15 min to Manhattan47594947IrisQueensSunnyside40.74271-73.92493Private room74262019-05-260.1315
131444015TIME SQUARE CHARMING ONE BED IN HELL'S KITCHEN,NYC8523790JohlexManhattanHell's Kitchen40.76682-73.98878Entire home/apt17030NaTNaN1188
28741020Voted #1 Location Quintessential 1BR W Village Apt45854238JohnManhattanWest Village40.73631-74.00611Entire home/apt2453512018-09-191.1210
334602077Spacious 1 bedroom apartment 15min from Manhattan261055465ReganQueensAstoria40.76424-73.92351Entire home/apt125312019-05-240.65113
423203149Big beautiful bedroom in huge Bushwick apartment143460MeganBrooklynBushwick40.69839-73.92044Private room65282019-06-230.5228
54402805LRG 2br BKLYN APT CLOSE TO TRAINS AND PARK22807362JennyBrooklynProspect-Lefferts Gardens40.66025-73.96270Entire home/apt120332018-08-280.05116
630070126✩Prime Renovated 1/1 Apartment in Upper East Side✩4968673SeanManhattanUpper East Side40.76831-73.95929Entire home/apt200522019-05-260.68171
734231172Fully renovated brick house floor in Brooklyn59642348KevinBrooklynSunset Park40.64550-74.01262Entire home/apt95192019-07-089.001106
85856760Renovated 1BR in exciting, convenient area29408349ChadManhattanChinatown40.71490-73.99976Entire home/apt179572017-04-180.1410
97929441Beautiful Loft w/ Waterfront View!1453898AnthonyBrooklynWilliamsburg40.71268-73.96676Private room10522322019-06-195.00364
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
199905192459Quiet Room in 4BR UWS Brownstone10677483GregManhattanUpper West Side40.80173-73.96625Private room7010NaTNaN10
199911327940Huge Gorgeous Park View Apartment!3290436HadarBrooklynFlatbush40.65335-73.96257Entire home/apt1203132016-08-270.282327
1999223612681Shared Room 1 Stop from Manhattan on the F Train55724558TaylorQueensLong Island City40.76006-73.94080Private room55422019-06-010.65589
1999334485745Midtown Manhattan Stunner - Private room261632622RoyaltonManhattanTheater District40.75491-73.98507Private room100132019-06-163.009318
1999425616250Stylish, spacious, private 1BR apt in Ditmas Park125396920AdamBrooklynFlatbush40.64314-73.95705Entire home/apt753102019-01-030.8410
199957094539Tranquil haven in bubbly Brooklyn2052211AdrianaBrooklynWindsor Terrace40.65360-73.97546Entire home/apt1431422016-08-270.04110
199964424261Large 1 BR with backyard on UWS3447311SarahManhattanUpper West Side40.80188-73.96808Entire home/apt2002222019-05-210.5010
199974545882Amazing studio/Loft with a backyard23569951KavehManhattanUpper East Side40.78110-73.94567Entire home/apt2203282019-05-230.501293
1999826518547U2 comfortable double bed sleeps 2 guests295128Carol GloriaBronxClason Point40.81225-73.85502Private room80142019-07-011.487365
1999933631782Private Bedroom in Williamsburg Apt!8569221AndiBrooklynWilliamsburg40.71829-73.95819Private room109332019-04-281.07297